Sensors 2012, 12(10), 13185-13211; doi:10.3390/s121013185
Article

Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer

Department of Electronics and Radio Engineering, Kyung Hee University, Yongin 446-701, Korea
* Author to whom correspondence should be addressed.
Received: 10 August 2012; in revised form: 19 September 2012 / Accepted: 19 September 2012 / Published: 27 September 2012
(This article belongs to the Section Physical Sensors)
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Abstract: In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM) technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.
Keywords: MEMS application; human motion recognition; non-parametric Bayesian inference; infinite Gaussian mixture model; Gibbs sampler

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MDPI and ACS Style

Ahmed, M.E.; Song, J.B. Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer. Sensors 2012, 12, 13185-13211.

AMA Style

Ahmed ME, Song JB. Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer. Sensors. 2012; 12(10):13185-13211.

Chicago/Turabian Style

Ahmed, M. Ejaz; Song, Ju Bin. 2012. "Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer." Sensors 12, no. 10: 13185-13211.

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